jpen.tune {JPEN} | R Documentation |
Returns optimal values of tuning parameters lambda and gamma which minimizes the K-fold crossvalidation error on
jpen.tune(Ytr, gama, lambda=NULL)
Ytr |
Ytr is matrix of observations. |
gama |
gama is vector of gamma values. gamma is non-negative. |
lambda |
lambda is vector of lambda values. lambda is non-negative. |
Returns the value of optimal tuning parameters. The function uses K-fold cross validation to select the best tuning parameter from among a set of of values of lambda and gamma.
Returns the optimal values of lambda and gamma.
Ashwini Maurya, Email: mauryaas@msu.edu.
A Well Conditioned and Sparse Estimate of Covariance and Inverse Covariance Matrix Using Joint Penalty. Submitted. http://arxiv.org/pdf/1412.7907v2.pdf
jpen
p=10;n=100;
Sig=diag(p);
y=rmvnorm(n,mean=rep(0,p),sigma=Sig);
gama=c(0.5,1.0);
opt=jpen.tune(Ytr=y,gama);